overview and documentation of the telus regional input

Transcription

Transportation Economic and Land Use System (TELUS)
Overview and Documentation of the TELUS
Economic Input-Output Model
M. H. Robison
Economic Modeling Specialists, Inc.
M. L. Lahr
Rutgers University, Center for Urban Policy Research
J. P. Berning
Economic Modeling Specialists, Inc.
November, 2004
Table of Contents
1. Introduction ................................................................................................................................ 1
1.1. Challenges Posed by the TELUS IO Component ............................................................2
2. The TELUS Multiregional IO (MRIO) Algorithm ................................................................... 4
2.1. Mathematics of the Single Region Model.........................................................................4
2.2. The Rutgers RPC Estimating Equation ............................................................................4
2.3. Mathematics of the Multiregional Model.........................................................................7
2.4. Data Sources for Model Construction ..............................................................................8
3. Selecting Regions for MRIO Modeling.................................................................................... 12
3.1. Central Place Theory.........................................................................................................13
3.2. The Problem of Spillovers and Feedbacks......................................................................14
3.3. Spatial Misspecification Error..........................................................................................17
4. Resources for Identifying Central Place Hierarchies.............................................................. 20
4.1. BEA Economic Areas ........................................................................................................20
4.2. Rand McNally Trading Areas ..........................................................................................20
4.3. National Transportation Analysis Regions (NTARs)...................................................21
5. Distance Matrices...................................................................................................................... 23
5.1. Development of Distance Matrices .................................................................................23
6. MPO Maps................................................................................................................................. 25
6.1. MPO Regions .....................................................................................................................25
Appendices ...................................................................................................................................... 26
Appendix A: MPO IO Models Developed In 2001 .................................................................26
Appendix B: MPO IO Models Developed In 2002..................................................................30
Appendix C: MPO IO Models Developed In 2003 .................................................................33
Appendix D: MPO IO Models Developed In 2004 .................................................................36
Appendix E: Phoenix MPO Distance Matrix ..........................................................................39
Appendix F: Map of Arkansas Meta-Region ...........................................................................40
Bibliography .................................................................................................................................... 41
Overview and Documentation of the TELUS Regional Input-Output Model
Page 1
1. Introduction
The present document represents a thorough rewrite of the earlier TELUS IO model
(Transportation, Economic and Land-Use System, Input-Output) documenting report: A
Regional Input-Output Model for Estimating Economic Impacts in TELUS: Role, Data Sources and
Technical Specifications, (H. Robison and K. Gneiting, November 26, 1999). The earlier report
presented data sources and methods for building single-region models and the outlines of an
algorithm for estimating interregional trade as needed for the inter-county and rest-of-state
impacts aspect of the TELUS IO component. The paper was presented at the TELUS IO Model
Peer Review Meeting at NJIT (New Jersey Institute of Technology) in November 1999. During
the 2001 EMSI-NJIT contract year, EMSI (Economic Modeling Specialists, Inc.) built models
reflecting the earlier documentation for the 105 MPOs (Metropolitan Planning Organizations)
shown in Appendix A.
Starting with the 2002 EMSI-NJIT contract year, models were built utilizing the Rutgers
University, CUPR R/ECON I-O (Center for Urban Policy Research) modeling system. The
R/ECON system has been custom-outfitted with the interregional trade algorithm utilized by
EMSI in building models the first year, i.e., models constructed during the 2001 EMSI-NJIT
contract year for the MPOs shown in Appendix A. Given the basic similarity in data sources
between the EMSI and R/ECON models, the resulting TELUS models should be fundamentally
the same.1 The 82 models completed in 2002, 79 models in 2003, and 74 models in 2004 are
shown in Appendices B, C, and D respectively.
1
One difference between the first year EMSI models and R/ECON models pertains to aggregation. Given
computer improvements since the 1999 design date of the original EMSI IO modeling software, the
R/ECON software permits construction of individual region models with the full sectoral detail of the U.S.
National IO model, approximately 500 sectors. In contrast, the original EMSI software required sectoral
aggregation down to the approximately 50 sectors directly impacted by transportation projects. The
original EMSI approach thereby results in a degree of aggregation error, though assuring that “first-order
aggregation error” will be zero (see: Morimoto, 1970).
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A key component of the TELUS IO model is a set of translators that capture the direct spending
effect of transportation construction projects. The TELUS IO model requires 100 translators for
each state, or 5,000 translators overall. Documentation of the translator assembly process
appears in a separate EMSI documents: TRANSLATOR DOCUMENTATION: Mapping
Transportation Construction and Maintenance Projects into Economic Sectors for Use in the
TELUS Multiregional Input-Output Model, (H. Robison and W. Webb, December, 2002) and
EMSI Process for Translator Development.
1.1. Challenges Posed by the TELUS IO Component
Constructing an IO component for TELUS presents special challenges. Impacts are to be
estimated and displayed for each MPO County, and for the rest of the host state.2 MPOs may
consist of many counties. The Dallas-Fort Worth MPO, for example, has 16 counties, requiring a
model with 17 regions; the 17th region represents the rest of the state of Texas. As discussed
below, theoretical considerations require additional sub-regional delineation. With current
regional IO modeling technique calling for single-region base models with approximately 500
industrial sectors, the size of a full multiregional IO model might be 10,000 x 10,000 or more in
the case of a 20-region model (500 sectors x 20 regions). Addressing the size of a multiregional
model alone is therefore a challenge.
Building TELUS IO components poses other challenges as well. While the procedures for
building single-region models are well established,3 the same cannot be said of multi-region
models. Previous applications have employed a type of gravity mechanism (see for example
2
TELUS displays job, income, and gross regional product impacts for five industrial sectors, construction,
manufacturing, retail/wholesale, services, and government/other. In addition, aggregate impacts are
shown for state, local and federal taxes. The various impact measures are displayed for the individual
counties of the MPO, the MPO as a whole (i.e., sum of county impacts), to the state economy outside the
MPO, and for the state as a whole (i.e., sum of MPO plus in-state but out-of-MPO impacts).
3
Nowadays, best practice calls for some variation on the econometric approach first proposed by Stevens
and others.
Page 3
Leontief and Strout, 1963 and Polenske, 1980), or they have assumed trade patterns based on
apparent hierarchical trade relationships (see for example Robison, 1984).
The approach
adopted for the TELUS IO module combines elements of both applications by applying a gravity
component to the study regions defined on principles of hierarchical trade theory.
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2. The TELUS Multiregional IO (MRIO) Algorithm
2.1. Mathematics of the Single Region Model
Equation (1) shows the basic transformation of the national IO coefficients matrix into a regional
IO coefficients matrix for region r.
{ }
Arr = γˆ rr A
(1)
where:
Arr = regional coefficients matrix
A = national coefficients matrix
A single superscripted r would normally suffice to denote a single region r. The adoption of dual
superscripts anticipates multiregional formulations described below. The pre-multiplying array
γˆ
is a diagonal matrix of regional purchase coefficients (RPC). In general, these illustrate the
portion of regional demand satisfied by regional supplies.
2.2. The Rutgers RPC Estimating Equation
Adjustments for interregional trade4 are made using techniques developed by Treyz and Stevens
(1985, pp. 553-554). In this paper, the authors show that they estimate the regional purchase
coefficients (RPC) – the proportion of demand for an industry’s goods or services that is fulfilled
by local suppliers – using the following equation (2):
4
These adjustments do not account for international exports.
⎧
⎫
⎪
⎪
⎪
⎪
n
−
1
⎪
⎪
ln ⎨
⎬ = ln k + α j Z j + ε
⎪ ⎡ ⎛ LS ⎞ ⎤ ⎪
i =1
⎟⎥ ⎪
⎪ ln ⎢ln ⎜⎜
⎟
⎪ ⎢⎣ ⎝ D ⎠ ⎥⎦ ⎪
⎩
⎭
∑
Page 5
(2)
Using standard OLS regression techniques where:
D = local demand for the industries production and estimated as discussed in the
previous section;
LS = the amount of local demand that is fulfilled by local supplies;
k = an intercept term;
Zj = one of the n explanatory variables listed in Table 1;
αj = the estimated parameter value associated with variable Zj.
This odd nonlinear functional form not only yields high correlations between the estimated and
actual values of the RPCs at the state level, but it also assures that the RPC value ranges strictly
between 0 and 1. The results of the Treyz and Stevens (1985) empirical implementation of this
equation are shown in Table 1. The table shows that total local industry demand (Z1), the
supply/demand ratio (Z2), the weight/value ratio of the good (Z3), the region’s size in square
miles (Z4), and the region’s average establishment size in terms of employees for the industry
compared to the nation’s (Z5) are the variables that influence the value of the RPC across all
regions and industries. The latter of these maintains the least leverage on RPC values.
Because the U.S. Department of Transportation’s 1977 Commodity Transportation Survey (CTS)
data used to estimate this equation were applied at the state level only, it is important for the
purposes of TELUS that the local industry demand, the supply/demand ratio, and the region’s
size in square miles are included in the equation because they allow the equation to extrapolate
the estimation of RPCs for areas smaller than states.
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It should also be noted here that the CTS data only cover manufactured goods. Thus, RPC
estimates for services are generally based on a truncation of each industry’s supply/demand ratio.
Table 1: Parameters for the Four-Digit Regional Purchase Coefficients
Variable
Intercept (k)
Parameter
t-statistic
21.33
Demand
0.18
8.73
Supply/demand
0.72
16.17
Weight/value
0.29
12.64
Percentage of U.S. land area
0.27
8.64
Establishments per employee relative to nation
0.12
2.54
Midwest Census Division
-0.12
-1.98
Plains Census Division
-0.61
-6.64
East South Central Census Division
-0.58
-7.93
Pacific Census Division
0.43
4.48
Tobacco industry (SIC 21)
1.05
2.84
Textile industry (SIC 22)
0.52
3.35
Furniture industry (SIC 25)
0.36
1.69
Printing and publishing industry (SIC 27)
0.81
3.89
-0.67
-3.38
Stone, clay, and glass industry (SIC 32)
0.24
2.56
Instruments industry (SIC 38)
0.37
1.95
-.064
-2.23
1.16
4.25
Rubber industry (SIC 30)
Flour and grain mill products industry (SIC 2041)
Electrical machinery, NEC (SIC 3599)
Note: F=58.687, with 1,348 degrees of freedom; R2=0.4405, but the R2
between the estimated RPCs (after transformation) and the actual values
from the CTS was 0.7413.
Page 7
2.3. Mathematics of the Multiregional Model
The algorithm for estimating multiregional IO coefficients is illustrated with the example of an
economy with three sub-regions. The combined region is referred to as the meta-region. The
rational for identifying meta-regions and constituent sub-regions is discussed in sections below.
The basic expression for transforming national model IO coefficients into multiregional IO
coefficients is illustrated for our hypothetical three-region meta-region in equation (3). The lead
matrix on the equation’s right is a 3x3-partitioned matrix, where each sub-matrix, denoted by
γˆ ,
is a diagonal sub-matrix.
⎧ Arr
⎪
⎪ sr
⎨A
⎪ tr
⎪A
⎩
Ars Art ⎪⎫ ⎪⎧γˆ rr γˆ rs γˆ rt ⎪⎫ ⎧⎪ A 0 0 ⎫⎪
Ass Ast ⎪⎬ = ⎪⎨γˆ sr γˆ ss γˆ st ⎪⎬ ⎪⎨ 0 A 0 ⎪⎬
Ats Att ⎪⎪ ⎪⎪γˆtr γˆts γˆtt ⎪⎪ ⎪⎪ 0 0 A⎪⎪
⎭
⎭⎩
⎩
Sub-matrices on the partition diagonal ( γˆ
rr
(3)
⎭
, γˆ ss , γˆ tt ) are the single region RPCs estimated as
described in the previous section. Sub-matrices on the off-diagonal convey multiregional RPCs.
As an example, the RPC
γˆ rs indicates the portion of region s overall demand satisfied by supplies
from region r.
The RPCs are estimated using gravity assumptions as follows. Again, using region r as a supplier
to region s, vectors of excess supply for each region are computed as follows:
E r = X r − Arr X r
where:
Xr = region r vector of total gross outputs
Er = region r vector of excess supplies
(4)
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Next, vectors of unmet demand are computed. For region r, these appear as:
M r = ⎡⎣ A − Arr ⎤⎦ X r
(5)
where:
Mr = region r vector of unmet demand.
Finally, multiregional RPCs are formed as follows:
α Eir M is
drsb
= αirs
(6)
where:
dij = travel time between region r and region s
b = distance exponent (assumed equal to 1)
α = gravitational constant.
The algorithm computes the gravitational constant
α
in such a manner that no region exports
(beyond the meta-region boundary) more than 95% of its production of any given industry, nor
does any region meet more than 95% of its demand for any commodity from suppliers located
within the meta-region.
2.4. Data Sources for Model Construction
The regional economic data on which the R/ECON I-O system is built are derived from federally
supplied data from a variety of sources listed below:
County Business Patterns Data, US Bureau of the Census, Department of Commerce,
http://www.census.gov:80/epcd/cbp/view/cbpview.html
Page 9
Earnings by Industry (Tables SA05 & CA05), Regional Economic Measurement Division,
Bureau
of
Economic
Analysis,
US
Department
of
Commerce,
http://www.bea.doc.gov/bea/regional/reis/
Wage and Salary Disbursements by Industry (Table SA07), Regional Economic
Measurement Division, Bureau of Economic Analysis, US Department of Commerce
Full- and Part-time Employment by Industry (Tables SA25 & CA25), Regional Economic
Measurement Division, Bureau of Economic Analysis, US Department of Commerce,
Gross State Product (GSP) Data, Bureau of Economic Analysis, US Department of
Commerce, http://www.bea.doc.gov/bea/regional/gsp/
Covered Employment and Wages (ES202 data), Bureau of Labor Statistics,
ftp://ftp.bls.gov/pub/special.requests/cew/
Value of Production by Commodity, Census of Agriculture, National Agricultural Statistics
Service, US Department of Agriculture, http://govinfo.kerr.orst.edu/ag-stateis.html/
Census of Government Finances, US Bureau of the Census, Department of Commerce,
http://www.census.gov:80/govs/www/cog.html
To produce earnings and total employment by region, County Business Patterns (CBP) data on
payroll and payroll employment,5 which are available at the four-digit Standard Industrial
Classification (SIC) level for non-agricultural and non-government sectors, are compiled for the
5
Payroll employment is the number of employees that receives regular wages and salaries. Hence, these
figures do not include business proprietors unless they receive wages as well as income that they accrue by
owning the business.
Page 10
specified geography.6 The CBP payroll and payroll employment data are subsequently enhanced
using BEA’s state-level Table SA05 earnings/payroll and Table SA25 total-employment /payrollemployment ratios at the two-digit SIC level. These ratios are then applied to the detailed sectors
in the economic model with which they are associated. Government enterprise and private
household employment are obtained separately from the ES202 files. Sub-national data on
agricultural income and employment by industry are derived using the region’s share of the
national value of production of the commodity. Thus, for agriculture only it is assumed that the
sector has the same average earnings per employee everywhere in the US.
To produce value-added and tax-revenue data, the value-added/earnings ratios by industry are
calculated from the two-digit SIC data and the data on earnings derived as discussed above.
Similar calculations from the same datasets are made to estimate indirect federal-governmenttax/earnings ratios and indirect state-and-local-government-tax/earnings ratios. The state-local
split of indirect business taxes and of personal taxes is calculated based on Census of Government
Finances data for the region. Wages net of taxes are estimated by calculating the share of
compensation from the GSP data files composed of the wages from BEA’s Table SA07, which are
also only available by state.
The estimated outputs by industry are derived from the labor-income coefficients of the
R/ECON I-O national I-O table and the earnings data discussed above. Lahr (2001) delves into a
more precise mathematical treatment of the techniques discussed here.
After the above data are collected, regional demand data by industry are estimated using the
techniques described in Treyz and Stevens (1985, equation 2). That is, in order to derive regional
demand by industry, the regional output estimates (obtained using the techniques discussed
6
The smaller a region is from an economic perspective, the greater are the number of the disclosure
problems in CBP data. Hence, before using the CBP data in R/ECON I-O, all disclosure problems were
filled in using a method like that described in Gerking et al. (2001).
Page 11
above) are first multiplied up the columns of the national direct-requirements matrix. The sums
of the rows of the resulting matrix are then obtained to get regional inter-industry demand. To
get total regional demand, of course, one must add to inter-industry demand the demand for
industry production placed by regional final demand i.e., locally based government operations,
regional households, and the region’s international exports. The product of regional output and
the national final-demand/output ratio estimates regional final demand by industry.
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3. Selecting Regions for MRIO Modeling
TELUS impact reporting requirements call for the estimation of impacts for each MPO County
and for the rest of the host state. Accordingly, single-region models must be constructed for
individual MPO counties, along with trade among these counties. But how should the rest-ofstate region, including trade between this region and the MPO counties be treated?
In general, rest-of-state areas will rarely appear as functioning economies appropriate for
treatment as stand-alone regions, but rather as doughnut areas with unconnected centers and
little internal cohesion. Correct treatment of rest-of-state areas will normally require that they be
viewed as composed of multiple, largely independent sub-regions. At a minimum then, trade
must be estimated among the rest-of-state sub-regions, and between these several sub-regions
and the counties of the MPO.
Unfortunately, the demands of the TELUS multiregional modeling problem do not stop here.
U.S. state and county boundaries are artifacts of 19th politics (Fox and Kumar, 1965). As a result,
economic boundaries routinely cross state lines, and this necessitates the inclusion of out-of-state
areas within the larger meta-region of the multiregional IO model. Omission of these out-ofstate areas, or failure to recognize the internal character of doughnut-shaped rest-of-state regions
results in predictable error in TELUS IO model impact estimates. This error is referred to as
spatial misspecification error. The next 4 sections present theory and guidelines to minimize
spatial misspecification error. The first section presents the outlines of central place theory, the
fundamental underpinning of regional definition.
The second and third sections provide
alternative views of spatial misspecification error issue. The first considers the problem as one of
spillovers and feedbacks, while the second looks at it in a more technical sense. The final section
provides the practical resources used to identify economic sub-regions.
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3.1. Central Place Theory
Central place theory (Christaller, 1966 and Losch, 1954) provides the foundation for
characterizing the regional trade hierarchy. Central Place theory views the regional landscape
with sub-regions defined and ordered according to the goods and services they provide to
themselves and to other sub-regions (Berry et al., 1988). Parr (1987) provides taxonomy of goods
and services in a central place hierarchy, distinguishing between central place and specialized
goods and services. Central place goods and services include items for which there is essentially
ubiquitous demand: groceries, consumer durables, movies, air travel, accounting, legal and
business services, and so on. Specialized goods and services are items for which production is
unique to particular regions: agricultural products, timber, input-oriented manufacturing
military installations, federal government offices, and so on.
Lower-order sub-regions supply their own lower-order central place goods and services and
obtain higher-order central place goods and services from higher-order sub-regions. Higherorder sub-regions supply their own lower-and higher-order central place goods and services.
There is no trade in central place goods and services between same-order sub-regions. Subregions at the bottom of the trade hierarchy (lowest-order sub-regions) derive their income from
the export of specialized goods to other sub-regions for processing or outside the larger region.
Higher-order sub-regions derive their income from the supply of higher-order central place
goods and services to lower-order sub-regions and from the export of specialized goods to other
sub-regions and to outside the larger region.
TELUS reporting requirements call for the estimation of impacts that spill beyond MPO
boundaries to the rest of the state. This proves to be a complex endeavor. For one thing, the rest
of the state is almost never a functioning regional economy, and must therefore be subdivided
into multiple sub-regions. Beyond this, state boundaries are rarely economic boundaries, and
this requires the inclusion of out-of-state areas in the encompassing meta-region.
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3.2. The Problem of Spillovers and Feedbacks
The size and choice of sub-regions within the larger meta-region depends on the character of the
broader area trade hierarchy of which the state economy is a part. States differ not only in their
mix of industries, resource endowments and such, but also in their underlying spatial structures,
i.e., number, size, placement and interconnectedness of cities and towns. Regional IO models
capture the spread of multiplier effects among industries, and when space is included, as in the
multiregional model, they capture the spread of multiplier effects across places as well. Failure to
properly capture this spatial aspect of multiplier diffusion can lead to problems in spillover
estimation.
Consider a hypothetical example. Figure 1 shows an MPO surrounded by a trade-dominated
hinterland. Imagine a transportation project in the MPO that entails input purchases within the
MPO plus input purchases outside the MPO at location A. The project will create jobs and
incomes both in the MPO and in the vicinity of location A. However, by virtue of the MPOs
trade dominance of the hinterland including location A, we can expect feedback job and income
effects to the MPO. Failure to recognize the region’s spatial structure, specifically the MPOs
dominance of location A, could result in the omission of feedback effects, and the accompanying
understatement of MPO economic impacts.
Figure 2 shows a variant on the case shown in Figure 1. This time the input supplier at location
A is in a neighboring state, and job and income effects in the vicinity of A are properly excluded
from the TELUS report. However, the feedback effect to the MPO is still in force, and failure to
recognize this would lead to an understatement of MPO impacts.
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A
MPO
Trade Area
Figure 1: An MPO and its trade partner (e.g. materials supplier B)
located within the same trade area
Figure 2: An MPO and its trade partner (e.g. materials supplier B)
located within the same trade area, but in two different states
Figure 3 illustrates another possibility. Suppose the supplier of project inputs is located at point
B, in the hinterland of a neighboring trade center. The path of spatial multiplier transmission in
this case leads to the neighboring center, rather than to the MPO. If the path is instead
erroneously led back to the MPO, overstated MPO impacts would result.
Page 16
Figure 3: An MPO and its trade partner (e.g. materials supplier B) located within
different (neighboring) trade areas
Figure 4: An MPO and its trade partner (e.g. materials supplier B) located within
different (neighboring) trade areas, but within the same state
A fourth and final case is illustrated in Figure 4. While point B is located in the MPO host state,
B’s dominating trade center is located in a neighboring state. In this case, the multiplier effects
leading from location B leak across the state boundary, and spill out-of-state. The impacts in the
Page 17
vicinity of B are properly reported, while the lower-to-higher-order multiplier effects are
properly excluded. Failure to recognize this feature of the local space economy would lead to
overstated impacts.
3.3. Spatial Misspecification Error
Spatial misspecification error occurs when single or multi-regional IO modeling algorithms are
applied to regions that are other than functional economies.
To illustrate consider the
hypothetical collection of political and economic regions depicted in Figure 5.
Political
boundaries appear with broad dashed lines, while economic boundaries appear with the solid
lines. For the sake of discussion, let us refer to the political regions as states.
The depiction shown in Figure 5 shows three states arrayed as though on a line, with empty space
elsewhere, and it implicitly conveys a two-order trade hierarchy. Without loss of generality, this
simplification illustrates the circumstances that result in spatial misspecification error. In reality,
of course, the plane is filled with regions, running in all directions, and trade hierarchies of order
greater than two.
Figure 5 explicitly shows three trade centers, A, B, and C. Our interest is in the state centered on
A, with political boundaries that include hinterland areas V and W. There are two general
conditions for spatial misspecification error and the state centered on A in Figure 5 exhibits both
of these conditions:
1. The state economically dominates an area belonging to a neighboring state, as illustrated
by A’s dominance of region X in Figure 5.
2. The state contains an area that is dominated by a center located in a neighboring state, as
illustrated by C’s dominance of region V in Figure 5.
Figure 5: Illustration of the two-order trade hierarchy (blue dashed lines
represent state or other administrative/political boundaries; solid red lines
represent boundaries of trade regions/areas)
Page 18
Page 19
The reason for the error is easily understood. The gravity method for estimating multiregional
trade operates on a supply-demand pooling mechanism. Surplus production in one region (i.e.,
production beyond local demand) is assumed available to meet otherwise unmet demand in
other regions, with near trade regions favored over distant trade regions. The solution of course
is to divide the larger area into component functional economies, and estimate the trade among
these smaller functional economic entities. This is the approach followed in constructing the
TELUS multiregional model.
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4. Resources for Identifying Central Place Hierarchies
4.1. BEA Economic Areas7
The U.S. Department of Commerce, Bureau of Economic Analysis divides the U.S. economy into
172 economic areas. Boundaries are drawn on economic rather than political or administrative
criteria. The work is clearly shaped by central place theory and the related notion of functional
economic areas (Fox and Kumar, 1965).
Each BEA economic area consists of a standard metropolitan statistical area (SMSA), or similar
area that serves as a center of trade, and the surrounding counties that are economically related
to the center. The delineation assures that each area will be relatively self-sufficient in the output
of its local service industries.
The 172 BEA economic areas are built-up from 348 smaller component economic areas (CEA).
CEAs are defined around a single economic node and the surrounding counties that are
economically related to the node. In a sense, therefore, a given BEA economic area can be
viewed as a three-order trade hierarchy, with the BEA core as the 3rd and highest order place,
followed by the centers of the CEAs (2nd order places), surrounded in turn by the dominated
CEA hinterlands.
4.2. Rand McNally Trading Areas
The Rand McNally Commercial Atlas and Marketing Guide (Rand McNally, 1999) provides an
alternative collection of central place-based trade areas. The elemental building blocks are 487
basic trading areas. These are areas surrounding at least one basic trading center. A basic trading
7
This description is excerpted from U.S. Department of Commerce, Bureau of Economic Analysis, 1975
and 1995.
Page 21
center is a city that serves as a center for the purchase of shopping goods. Basic trading areas
have apparel stores, general merchandise stores, and specialized services such as medical care,
entertainment, higher education and a daily newspaper. These follow county lines and are drawn
to include the county or counties whose residents make the bulk of their shopping goods
purchases in the area’s basic trading center(s) or its suburbs.
Moving up the trade hierarchy one level, the 487 basic trading areas are collected to into 47
major trading areas, each centered on its own major trading center. A major trading center is a
city that serves as one of the trading area’s primary centers for wholesaling, distribution, banking,
and specialized services such as advertising. The major trading area’s boundaries are determined
after “an intensive study” that considers such factors as physiography, population distributions,
newspaper circulation, economic activities, highway facilities, railroad services, suburban
transportation, and field reports of experienced sales analysts.
4.3. National Transportation Analysis Regions (NTARs)8
The U.S. Department of Transportation (DOT) has defined NTAR regions to “collect and
publish information on the interregional movements of goods, including the Commodity Flow
Survey (CFS).” NTAR boundaries, like those of the BEA economic areas and Rand McNally
trade areas, are constructed on principles grounded in central place theory. NTAR boundaries
recognize that transportation demand is molded by economic, social, and physical forces that
generally ignore political boundaries. Mountain ranges and the hinterlands of major economic
centers are just two of the many characteristics that affect patterns of transportation supply and
demand and have little correlation to state lines.
NTAR regions are drawn according to
functional geography rather than by state or other political units.
8
This section is excerpted from the U.S. Department of Transportation, Bureau of Transportation
Statistics website: http://www.bts.gov/programs/cfs/ntars/ntars.html
Page 22
NTARs are based on centers of population or economic activity that account for most of the
origins, destinations, or transfers of long-distance passenger and commodity movements. Each
region is defined to encompass the hinterland of the region's terminals for long distance
transportation. Where hinterlands of closely neighboring centers substantially overlap, the
regions are combined.
NTARs are defined as combinations of BEA economic areas, in part to be less numerous, and in
part to eliminate overlapping hinterlands. The result is 89 NTAR regions that reflect larger
centers and hinterlands than the 172 BEA economic areas, though in many cases smaller centers
and hinterlands than the 47 Rand McNally major trading areas.
Page 23
5. Distance Matrices
5.1. Development of Distance Matrices
To incorporate into the models the gravity mechanism that was previously mentioned, distance
matrices were built for each of the 85 MPO models. The distance matrices describe the average
time required to travel between each of the locations/regions used to build an MPO model. The
distance and the average travel time between locations/regions are used to determine the average
miles per hour. An adjustment is made to the miles per hour to calibrate for travel difficulty.
Unimpeded travel, as might be expected along an interstate highway, receives a degree of
difficulty of 1, which does not adjust the speed of travel. Moderately difficult travel receives a
degree of difficulty of 2 and the speed of travel is discounted by 20 percent. With travel that is
considered to be very difficult, such as in a highly congested city, the speed of travel is cut in half.
Then, an adjusted travel time is calculated using the distance and the adjusted miles per hour.
Travel within a location/region is estimated to be an average distance of 20 miles and is also
adjusted for a degree of difficulty. Travel to and from certain regions was estimated by selecting
a central location in the region as a reference point. For example, the region Jackson, AL (which
includes counties in Alabama that are affected by Jackson, MS) has Demopolis, AL as the
reference point for the distance matrix calculations.
In order to discriminate between economic and political regions, as described in Section 3,
geographic regions were segmented to create the references used in the distance matrix. As an
example, Phoenix MPO has a distance matrix comprised of 12 cities/regions, including the single
MPO county Maricopa (shown in Appendix E), resulting in a 12 x 12. The matrix is built to
define the travel times between each pair of locations.
To account for economic
interrelationships between different political (administrative) regions, the nearby Albuquerque
economic region was divided into two distinct segments: Albuquerque, New Mexico (denoted as
Page 24
AlbuquerqueAZ, NM) and Albuquerque, Arizona (denoted as Albuquerque, AZ). The former
represents the economic influence of the city of Albuquerque, NM on Phoenix, which is
contained within the political boundary of the State of New Mexico. The latter represents the
economic influence of the city of Albuquerque on Phoenix, which is contained within the
political boundary of the State of Arizona. The significance of this distinction was outlined in
Section 3.
These segmented regions were then used to estimate the gravity mechanism. As can be seen in
the Phoenix MPO distance matrix (Appendix E), adjusted travel time between AlbuquerqueAZ,
NM and Las VegasAZ, NV (which represents the economic influence of Las Vegas, NV on
Phoenix, which is contained in Nevada) is 10.5 hours. Adjusted travel time within Maricopa
County is 0.48 hours (20 miles of travel distance at an average of 50 mph, adjusted for a degree of
difficulty of 1.2).
Page 25
6. MPO Maps
6.1. MPO Regions
The maps of all states containing MPOs were created to highlight the economic and political
regions. An example of the Arkansas state map is shown in Appendix F. The outline of
Arkansas is represented by the thicker black lines, with portions of surrounding states being
included as necessary. The economic meta-region consists of sub-regions delineated by different
colors. The center of each sub-region is labeled accordingly. For example, the economic region
of Memphis, TN extends across Alabama, Arkansas, Kentucky, Louisiana, Missouri, and, of
course, Tennessee. This region is shown in Appendix F as the light blue cross-hatched region
labeled ‘Memphis’. This map describes the meta-region for all MPOs in Arkansas, and also aids
in designing the distance matrices described in the previous section.
Page 26
Appendices
Appendix A: MPO IO Models Developed In 2001
#
MPO Name
State
City
1
Bay County Transportation Planning Organization
FL
Pensacola
2
Brevard MPO
FL
Viera
3
Broward County MPO
FL
Fort Lauderdale
4
Capital Region Transportation Planning Agency
FL
Tallahassee
5
Charlotte County - Punta Gorda MPO
FL
Punta Gorda
6
Collier County MPO
FL
Naples
7
First Coast MPO
FL
Jacksonville
8
Florida-Alabama Transportation Planning Organization
FL
Pensacola
9
Hernando County MPO
FL
Brooksville
10
Hillsborough County MPO
FL
Tampa
11
Indian River County MPO
FL
Vero Beach
12
Lee County MPO
FL
Fort Myers
13
Martin County MPO
FL
Stuart
14
METROPLAN Orlando
FL
Orlando
15
Metropolitan Transportation Planning Organization
FL
Gainesville
16
Miami-Dade MPO
FL
Miami
17
Ocala - Marion County Transportation Planning Organization
FL
Ocala
18
Okaloosa-Walton Transportation Planning Organization
FL
Pensacola
19
Palm Beach County MPO
FL
West Palm Beach
20
Pasco County MPO
FL
New Port Richey
21
Pinellas County MPO
FL
Clearwater
22
Polk County Transportation Planning Organization
FL
Bartow
23
Sarasota-Manatee MPO
FL
Sarasota
24
St. Lucie MPO
FL
Fort Pierce
25
Volusia County MPO
FL
Daytona Beach
26
Black Hawk Metropolitan Area Transportation Policy Board
IA
Waterloo
27
Corridor Metropolitan Planning Organization
IA
Cedar Rapids
28
Des Moines Area MPO
IA
Urbandale
29
East Central Intergovernmental Association
IA
Dubuque
Page 27
MPO IO Models Developed In 2001 (Continued)
#
MPO Name
State
City
30
Johnson County COG
IA
Iowa City
31
Sioux City MPO
IA
Sioux City
32
Bannock Planning Organization
ID
Pocatello
33
Bonneville MPO
ID
Idaho Falls
34
Community Planning Association of Southwestern Idaho
ID
Meridian
35
Bi-State Regional Commission
IL
Rock Island
36
Champaign County Regional Planning Commission
IL
Urbana
37
Decatur Area Transportation Study
IL
Decatur
38
Kankakee County Regional Planning Commission
IL
Kankakee
39
McLean County Regional Planning Commission
IL
Bloomington
40
Rockford Area Transportation Study
IL
Rockford
41
Springfield-Sangamon County Regional Planning Commission
IL
Springfield
42
The Chicago Metropolitan Agency for Planning (CMAP)
IL
Chicago
43
Tri-County Regional Planning Commission
IL
Peoria
44
Berkshire MPO
MA
Pittsfield
45
Boston MPO
MA
Boston
46
Cape Cod MPO
MA
Barnstable
47
Central Massachusetts MPO
MA
Worcester
48
Merrimack Valley MPO
MA
Haverhill
49
Montachusett MPO
MA
Fitchburg
50
Northern Middlesex MPO
MA
Lowell
51
Old Colony MPO
MA
Brockton
52
Pioneer Valley MPO
MA
West Springfield
53
Southeast Michigan COG
MI
Detroit
54
Columbia Area Transportation Study Organization
MO
Columbia
55
East-West Gateway Council of Government
MO
St. Louis
56
Joplin Area Transportation Study Organization
MO
Joplin
57
Mid-Amercia Regional Council
MO
Kansas City
58
Ozarks Transportation Organization
MO
Springfield
59
St. Joseph Area Transportation Study Organization
MO
St. Joseph
60
Capital Area MPO
NC
Raleigh
61
Mecklenburg-Union MPO
NC
Charlotte
62
Nashua Regional Planning Commission
NH
Nashua
Page 28
#
MPO Name
State
City
63
Rockingham Planning Commission
NH
Exeter
64
Southern New Hampshire Planning Commission
NH
Manchester
65
Strafford Regional Planning Commission
NH
Dover
66
Las Cruces MPO
NM
Las Cruces
67
Mid-Region COG
NM
Albuquerque
68
Santa Fe MPO
NM
Santa Fe
69
Adirondack/Glens Falls Transportation Council
NY
Fort Edward
70
Binghamton Metropolitan Transportation Study
NY
Binghamton
71
Capital District Transportation Committee
NY
Albany
72
Elmira-Chemung Transportation Council
NY
Elmira
73
Genesee Transportation Council
NY
Rochester
74
Greater Buffalo-Niagara Regional Transportation Council
NY
Buffalo
75
Herkimer-Oneida Counties Transportation Study
NY
Utica
76
Ithaca-Tompkins County Transportation Council
NY
Ithaca
77
New York Metropolitan Transportation Council
NY
New York
78
Orange County Transportation Council
NY
Goshen
79
Poughkeepsie-Dutchess County Transportation Council
NY
Poughkeepsie
80
Syracuse Metropolitan Transportation Council
NY
Syracuse
81
Akron Metropolitan Area Transportation Study
OH
Akron
82
Brook-Hancock-Jefferson Metropolitan Planning Commission
OH
Steubenville
83
Cincinnati-Northern Kentucky MPO
OH
Cincinnati
84
Clark County-Springfield Transportation Study
OH
Springfield
85
Eastgate Regional COG
OH
Austintown
86
Licking County Area Transportation Study
OH
Newark
87
Lima-Allen County Regional Planning Commission
OH
Lima
88
Miami Valley Regional Planning Commission
OH
Dayton
89
Mid-Ohio Regional Planning Commission
OH
Columbus
90
Northeast Ohio Areawide Coordinating Agency
OH
Cleveland
91
Richland County Regional Planning Commission
OH
Mansfield
92
Stark County Area Transportation Study
OH
Canton
93
Toledo Metropolitan Area COG
OH
Toledo
94
Bristol MPO
TN
Bristol
95
Chattanooga Urban Area MPO
TN
Chattanooga
Page 29
#
MPO Name
State
City
96
Clarksville Urbanized Area MPO
TN
Clarksville
97
Jackson Urban Area MPO
TN
Jackson
98
Johnson City Metropolitan Transportation Planning Organization
TN
Johnson City
99
Kingsport MPO
TN
Kingsport
100
Knoxville Regional Transportation Planning Organization
TN
Knoxville
101
Memphis Urban Area MPO
TN
Memphis
102
Nashville Area MPO
TN
Nashville
103
Chittenden County MPO
VT
South Burlington
Page 30
Appendix B: MPO IO Models Developed In 2002
#
MPO Name
State
City
104
Auburn - Opelika MPO
AL
Opelika
105
Birmingham MPO
AL
Birmingham
106
Calhoun Area Transportation Study
AL
Anniston
107
Decatur MPO
AL
Decatur
108
Gadsden-Etowah MPO
AL
Gadsden
109
Huntsville Area Transportation Study
AL
Huntsville
110
Mobile Area Transportation Study
AL
Mobile
111
Montgomery Area MPO
AL
Montgomery
112
Shoals Area MPO
AL
Muscle Shoals
113
South Wiregrass Area MPO
AL
Dothan
114
Tuscaloosa Area MPO
AL
Northport
115
Flagstaff MPO
AZ
Flagstaff
116
Maricopa Association of Governments
AZ
Phoenix
117
Pima Association of Governments
AZ
Tucson
118
Yuma MPO
AZ
Yuma
119
Capital Region COG
CT
Hartford
120
Central Connecticut Regional Planning Agency
CT
Bristol
121
Council of Governments of the Central Naugatuck Valley
CT
Waterbury
122
Greater Bridgeport / Valley MPO
CT
Bridgeport
123
Housatonic Valley Council of Elected Officials
CT
Brookfield
124
Midstate Regional Planning Agency
CT
Middletown
125
South Central Regional COG
CT
North Haven
126
South Western Region MPO
CT
Stamford
127
Southeastern Connecticut COG
CT
Norwich
128
Burlington-Graham MPO
NC
Burlington
129
Cabarrus-South Rowan Urban Area MPO
NC
Concord
130
Durham-Chapel Hill-Carrboro MPO
NC
Durham
131
Fayetteville Area MPO
NC
Fayetteville
132
French Broad River MPO
NC
Asheville
133
Gaston Urban Area MPO
NC
Gastonia
134
Goldsboro Urban Area MPO
NC
Goldsboro
Page 31
#
MPO Name
State
City
135
Greater Hickory MPO
NC
Hickory
136
Greensboro Urban Area MPO
NC
Greensboro
137
Greenville Urban Area MPO
NC
Greenville
138
High Point Urban Area MPO
NC
High Point
139
Rocky Mount Urban Area MPO
NC
Rocky Mount
140
Wilmington Urban Area MPO
NC
Wilmington
141
Winston-Salem Urban Area MPO
NC
Winston-Salem
142
Anderson Area Transportation Study
SC
Anderson
143
Charleston Area Transportation Study
SC
North Charleston
144
Columbia Area Transportation Study
SC
Columbia
145
Florence Area Transportation Study
SC
Florence
146
Grand-Strand Area Transportation Study
SC
Georgetown
147
Greenville-Pickens Area Transportation Study
SC
Greenville
148
Rock Hill-Fort Mill Area Transportation Study
SC
Rock Hill
149
Spartanburg Area Transportation Study
SC
Spartanburg
150
Sumter Urban Area Transportation Study
SC
Sumter
151
Rapid City Area MPO
SD
Rapid City
152
South Eastern COG
SD
Sioux Falls
153
Abilene MPO
TX
Abilene
154
Amarillo MPO
TX
Amarillo
155
Brownsville MPO
TX
Brownsville
156
Bryan-College Station MPO
TX
Bryan
157
Capital Area MPO
TX
Austin
158
Corpus Christi MPO
TX
Corpus Christi
159
El Paso MPO
TX
El Paso
160
Hidalgo County MPO
TX
McAllen
161
Houston-Galveston Area Council
TX
Houston
162
Jefferson-Orange-Hardin Regional Transportation Study
TX
Beaumont
163
Killeen-Temple Urban Transportation Study
TX
Belton
164
Laredo Urban Transportation Study
TX
Laredo
165
Longview MPO
TX
Longview
166
Lubbock MPO
TX
Lubbock
167
Midland-Odessa Transportation Organization
TX
Midland
Page 32
#
MPO Name
State
City
168
North Central Texas COG
TX
Arlington
169
San Angelo MPO
TX
San Angelo
170
San Antonio-Bexar County MPO
TX
San Antonio
171
Sherman-Denison MPO
TX
Sherman
172
Texarkana MPO
TX
Texarkana
173
Tyler Urban Transportation Study MPO
TX
Tyler
174
Victoria MPO
TX
Victoria
175
Wichita Falls MPO
TX
Wichita Falls
176
Cache MPO
UT
Logan
177
Mountainland Association of Governments
UT
Orem
178
Wasatch Front Regional Council
UT
Salt Lake City
179
BCKP Regional Intergovernmental Council
WV
South Charleston
180
Belmont-Ohio-Marshall Transportation Study
WV
Wheeling
181
KYOVA Interstate Planning Commission
WV
Huntington
182
Wood-Washington-Wirt Interstate Planning Commission
WV
Parkersburg
183
Casper Area MPO
WY
Casper
184
Cheyenne MPO
WY
Cheyenne
Page 33
Appendix C: MPO IO Models Developed In 2003
#
MPO Name
State
City
185
Bi-State MPO
AR
Fort Smith
186
Metroplan
AR
Little Rock
187
Northwest Arkansas Regional Transportation Study
AR
Springdale
188
Southeast Arkansas Regional Planning Commission
AR
Pine Bluff
189
West Memphis Area Transportation Study
AR
West Memphis
190
National Capital Region Transportation Planning Board
DC
Washington
191
Dover / Kent County MPO
DE
Dover
192
Wilmington Area Planning Council
DE
Newark
193
Area Plan Commission of Tippecanoe County
IN
Lafayette
194
Bloomington Area Transportation Study
IN
Bloomington
195
Delaware-Muncie Metropolitan Plan Commission
IN
Muncie
196
Evansville MPO
IN
Evansville
197
Indianapolis MPO
IN
Indianapolis
198
Kokomo & Howard County Governmental Coordinating Council
IN
Kokomo
199
Madison County COG
IN
Anderson
200
Michiana Area COG
IN
South Bend
201
Northeastern Indiana Regional Coordinating Council
IN
Ft. Wayne
202
Northwest Indiana Regional Planning Commission
IN
Portage
203
West Central Indiana Economic Development District, Inc.
IN
Terre Haute
204
Lawrence-Douglas County Metropolitan Planning Office
KS
Lawrence
205
Topeka-Shawnee County Metropolitan Planning Commission
KS
Topeka
206
Wichita Area MPO
KS
Wichita
207
Southeastern Regional Planning & Economic Development District
MA
Taunton
208
Baltimore Regional Transportation Board
MD
Baltimore
209
Cumberland MPO
MD
Cumberland
210
Hagerstown-Eastern Panhandle MPO
MD
Hagerstown
211
Battle Creek Area Transportation Study
MI
Springfield
212
Bay City Area Transportation Study
MI
Bay City
213
Genesee County Metropolitan Planning Commission
MI
Flint
214
Grand Valley Metropolitan Council
MI
Grand Rapids
215
Kalamazoo Area Transportation Study
MI
Kalamazoo
Page 34
#
MPO Name
State
City
216
Macatawa Area Coordinating Council
MI
Holland
217
Region 2 Planning Commission
MI
Jackson
218
Saginaw Metropolitan Area Transportation Study
MI
Saginaw
219
Southwestern Michigan Commission
MI
Benton Harbor
220
Tri-County Regional Planning Commission
MI
Lansing
221
Western Michigan Shoreline Regional Development Commission
MI
Muskegon
222
Central Mississippi Planning & Development District
MS
Jackson
223
Gulf Regional Planning Commission
MS
Gulfport
224
Hattiesburg-Petal-Forrest-Lamar MPO
MS
Hattiesburg
225
Lincoln MPO
NE
Lincoln
226
Metropolitan Area Planning Agency
NE
Omaha
227
North Jersey Transportation Planning Authority
NJ
Newark
228
South Jersey Transportation Planning Organization
NJ
Vineland
229
Blair County Planning Commission
PA
Altoona
230
Cambria County MPO
PA
Ebensburg
231
Centre County MPO
PA
State College
232
Delaware Valley Regional Planning Commission
PA
Philadelphia
233
Erie MPO
PA
Erie
234
Harrisburg Area Transportation Study
PA
Harrisburg
235
Lackawanna-Luzerne Transportation Study
PA
Scranton
236
Lancaster County Transportation Coordinating Committee
PA
Lancaster
237
Lehigh Valley Transportation Study
PA
Allentown
238
Reading Area Transportation Study
PA
Reading
239
Shenango Valley Area Transportation Study
PA
Hermitage
240
Southwestern Pennsylvania Commission
PA
Pittsburgh
241
Williamsport Area Transportation Study
PA
Williamsport
242
York Area MPO
PA
York
243
State Planning Council
RI
Providence
244
Harlingen-San Benito MPO
TX
Harlingen
245
Central Virginia MPO
VA
Lynchburg
246
Charlottesville-Albemarle MPO
VA
Charlottesville
247
Danville MPO
VA
Martinsville
248
Fredericksburg Area MPO
VA
Fredericksburg
Page 35
#
MPO Name
State
City
249
Hampton Roads MPO
VA
Chesapeake
250
Richmond Area MPO
VA
Richmond
251
Roanoke Valley MPO
VA
Roanoke
252
Tri Cities Area MPO
VA
Petersburg
253
Tri-Cities Metropolitan Area Transportation Study
WA
Richland
254
Chippewa-Eau Claire MPO
WI
Eau Claire
255
East Central Wisconsin Regional Planning Commission
WI
Menasha
256
Green Bay MPO
WI
Green Bay
257
Janesville Area MPO
WI
Janesville
258
La Crosse Area Planning Committee
WI
La Crosse
259
Madison Area MPO
WI
Madison
260
Marathon County Metropolitan Planning Commission
WI
Wausau
261
Sheboygan MPO
WI
Green Bay
262
Southeastern Wisconsin Regional Planning Commission
WI
Waukesha
263
State Line Area Transportation Study
WI
Beloit
Page 36
Appendix D: MPO IO Models Developed In 2004
#
MPO Name
State
City
264
Anchorage Metropolitan Area Transportation Solutions
AK
Anchorage
265
Association of Monterey Bay Area Governments
CA
Marina
266
Bay Area MPO
CA
Oakland
267
Butte County Association of Governments
CA
Chico
268
Council of Fresno County Goverrnments
CA
Fresno
269
Kern COG
CA
Bakersfield
270
Merced County Association of Governments
CA
Merced
271
Sacramento Area COG
CA
Sacramento
272
San Diego Association of Governments
CA
San Diego
273
San Joaquin COG
CA
Stockton
274
San Luis Obispo COG
CA
San Luis Obispo
275
Santa Barbara County Association of Governments
CA
Santa Barbara
276
Shasta County Regional Transportation Planning Agency
CA
Redding
277
Southern California Association of Governments
CA
Los Angeles
278
Stanislaus COG
CA
Modesto
279
Tulare County Association of Governments
CA
Visalia
280
Denver Regional COG
CO
Denver
281
Grand Junction / Mesa County MPO
CO
Grand Junction
282
North Front Range MPO
CO
Fort Collins
283
Pikes Peak Area COG
CO
Colorado Springs
284
Pueblo Area COG
CO
Pueblo
285
Atlanta Regional Commission
GA
Atlanta
286
Augusta Regional Transportation Study
GA
Augusta
287
Brunswick Area Transportation Study
GA
Brunswick
288
Chatham Urban Transportation Study
GA
Savannah
289
Columbus-Phenix City Transportation Study
GA
Columbus
290
Dougherty Area Regional Transportation Study
GA
Albany
291
Floyd-Rome Urban Transportation Study
GA
Rome
292
Macon Area Transportation Study
GA
Macon
293
Madison Athens-Clarke Oconee Regional Transportation Study
GA
Athens
294
Warner Robins Area Transportation Study
GA
Warner Robins
Page 37
#
MPO Name
State
City
295
Oahu MPO
HI
Honolulu
296
Lexington Area MPO
KY
Lexington
297
Louisville Area MPO
KY
Louisville
298
Owensboro-Daviess County MPO
KY
Owensboro
299
Alexandria MPO
LA
Alexandria
300
Capital Regional Planning Commission
LA
Baton Rouge
301
Houma-Thibodaux MPO
LA
Houma
302
Imperial Calcasieu Regional Planning & Development Commission
LA
Lake Charles
303
Lafayette Area MPO
LA
Lafayette
304
Northwest Louisiana COG
LA
Shreveport
305
Ouachata Council of Governments
LA
Monroe
306
Regional Planning Commission
LA
New Orleans
307
Androscoggin Transportation Resource Center
ME
Auburn
308
Bangor Area Comprehensive Transportation System
ME
Bangor
309
Kittery Area Comprehensive Transportation Study
ME
Springvale
310
Portland Area Comprehensive Transportation Committee
ME
Portland
311
Duluth-Superior Metropolitan Interstate Committee
MN
Duluth
312
Metropolitan Council
MN
St. Paul
313
Rochester-Olmsted COG
MN
Rochester
314
St. Cloud Area Planning Organization
MN
St. Cloud
315
Great Falls MPO
MT
Great Falls
316
Missoula Transportation Policy Coordinating Committee
MT
Missoula
317
Yellowstone County Planning Board
MT
Billings
318
Bismarck-Mandan MPO
ND
Bismark
319
Fargo-Morehead Metropolitan COG
ND
Fargo
320
Grand Forks-East Grand Forks MPO
ND
Grand Forks
321
Regional Transportation Commission of Southern Nevada
NV
Las Vegas
322
Regional Transportation Commission of Washoe County
NV
Reno
323
Tahoe MPO
NV
Stateline
324
Association of Central Oklahoma Governments
OK
Oklahoma City
325
Indian Nations COG
OK
Tulsa
326
Lawton MPO
OK
Lawton
327
Central Lane MPO
OR
Eugene
Page 38
#
MPO Name
State
City
328
Metro
OR
Portland
329
Rogue Valley COG
OR
Central Point
330
Salem-Keizer Area Transportation Study
OR
Salem
331
Longview-Kelso-Rainier MPO
WA
Kelso
332
Puget Sound Regional Council
WA
Seattle
333
Southwest Washington Regional Transportation Council
WA
Vancouver
334
Spokane Regional Transportation Council
WA
Spokane
335
Thurston Regional Planning Council
WA
Olympia
336
Whatcom COG
WA
Bellingham
337
Yakima Valley MPO
WA
Yakima
Page 39
Z
Tusc
on, A
, NM
Phoe
nixA
Z
Z
Maricopa (AZ)
0.48
6.30
9.90
3.60
6.25
4.80
7.50
2.40
0.60
6.60
3.00
Albuquerque, AZ
6.30
0.40
3.00
2.25
5.50
5.25
7.25
7.00 11.70 5.40
7.25
7.50
AlbuquerqueAZ, NM
9.90
3.00
0.48
5.50
8.50
8.25 10.50 10.00 15.30 9.30
5.00
9.30
Flagstaff, AZ
3.60
2.25
5.50
0.40
3.25
3.00
5.00
4.75
7.25
5.10
FlagstaffAZ, UT
6.25
5.50
8.50
3.25
0.40
6.25
5.00
8.00 10.50 5.50 10.50 7.50
Las Vegas, AZ
4.80
5.25
8.25
3.00
6.25
0.40
2.70
3.00
5.40
4.50
las VegasAZ, NV
7.50
7.25 10.50 5.00
5.00
2.70
0.48
4.50
4.50
7.20 10.75 9.60
Los Angeles, AZ
2.40
7.00 10.00 4.75
8.00
3.00
4.50
0.40
3.60
3.30
Los AngelesAZ, CA
5.70 11.70 15.30 9.00 10.50 5.40
4.50
3.60
0.48
6.30 12.00 8.70
Phoenix2, AZ
0.60
5.40
9.30
2.70
7.20
3.30
6.30
0.48
5.70
2.40
PhoenixAZ, NM
6.60
7.25
5.00
7.25 10.50 8.75 10.75 8.70 12.00 5.70
0.40
2.75
Tuscon, AZ
3.00
7.50
9.30
5.10
2.75
0.48
5.50
7.50
4.50
6.90
la s V
Phoe
nix2,
A
A Z, C
Los A
ngele
s
, AZ
Los A
ngele
s
Z, NV
egasA
Las V
egas,
AZ
F la g s
taffA
Z, UT
A
, NM
F la g s
taff, A
Z
ueAZ
A lb u
querq
ue, A
Z
A lb u
querq
Region Name
Mari
copa
(
AZ)
Appendix E: Phoenix MPO Distance Matrix
9.60
5.70
5.70
9.00
8.70
2.70
2.40
8.75
8.70
6.90
5.70
Appendix F: Map of Arkansas Meta-Region
Page 40
Page 41
Bibliography
Berry, B. J. L., J. B. Parr, B. J. Epstein, A. Ghosh, and R. H. T. Smith, 1988. Market Centers and
Retail Location, Theory and Applications. (Englewood Cliffs, Prentice Hall).
Christaller, W., 1966. Central Places in Southern Germany (C.W. Baskins, trans.). Englewood
Cliffs, NJ, Prentice Hall.
Fox, K. A. and K. Kumar. 1965. "The Functional Economic Area: Delineation and Implications
for Economic Analysis and Policy," Papers of Regional Science Association, 15, 57-85.
Gerkins, S. D., A.Isserman, W. Hamilton, T. Pickton, O.Smirnov, and D. Sorenson, 2001. “Antisuppressants and the creation and use of nonsurvey regional input-output models”. M.L. Lahr
and R. E. Miller, (ed.). Regional science perspectives in economic analysis: A festschrift in memory
of Benjamin H. Stevens. 379-306, Amsterdam: Elsevier Science.
Leontief, W. W. and A. Strout, 1963. "Multiregional Input-Output Analysis”, p.129-161, in
Input-Output Economics, 2nd Ed. Oxford University Press.
Losch, A., 1954. The Economics of Location (W. H. Woglom and W. F. Stolper, trans.). New
Haven: Yale University Press.
Morimoto, Y., 1970.”On Aggregation Problems in Input-Output Analysis”, Review of Economic
Studies, 119-126.
Parr, J. B., 1987. "Interaction in an Urban System: Aspects of Trade and Commuting," Economic
Geography, 63(3), 223-240.
Page 42
Polenske, K. R., 1980. The United States Multiregional Input-Output Accounts and Model,
(Lexington, MA, D.C. Heath).
Robison, M. H., J. R. Hamilton, K. P. Connaughton, N. Meyer and R. Coupal. 1994. "Spatial
Diffusion of Economic Impacts and Development Benefits in Hierarchically Structured Trade
Regions: An Empirical Application of Central Place-Based Input-Output Analysis," Review of
Regional Studies, 23(3), 307-326.
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19: 547-562.

overview and documentation of the telus regional input

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